In [1]:
# benötigte bib importieren

import pandas as pd
import numpy as np  
import plotly.io as pio
import plotly.express as px 
import seaborn as sns 

pio.renderers.default = "notebook"
In [2]:
# datensatz laden mit pickle
file_input = "df_after_preprocessing.pkl"
df_cf = pd.read_pickle(file_input)
df_cf.head(5)
Out[2]:
funded_amount loan_amount activity sector use country_code country currency term_in_months lender_count repayment_interval borrower_male_count borrower_female_count funding_pro_lender
0 300.0 300.0 Fruits & Vegetables Food To buy seasonal, fresh fruits to sell. PK Pakistan PKR 12 12 irregular 0 1 25.00000
1 575.0 575.0 Rickshaw Transportation to repair and maintain the auto rickshaw used ... PK Pakistan PKR 11 14 irregular 0 2 41.07143
2 150.0 150.0 Transportation Transportation To repair their old cycle-van and buy another ... IN India INR 43 6 bullet 0 1 25.00000
3 200.0 200.0 Embroidery Arts to purchase an embroidery machine and a variet... PK Pakistan PKR 11 8 irregular 0 1 25.00000
4 400.0 400.0 Milk Sales Food to purchase one buffalo. PK Pakistan PKR 14 16 monthly 0 1 25.00000

Visualisierung¶

Thema 1: Are projects reached the target?¶

In [3]:
df_group1 = df_cf.loc[:, ["funded_amount", "loan_amount"]]
df_group1["amount_reached"] = (df_group1.funded_amount >= df_group1.loan_amount)
df_group1
Out[3]:
funded_amount loan_amount amount_reached
0 300.0 300.0 True
1 575.0 575.0 True
2 150.0 150.0 True
3 200.0 200.0 True
4 400.0 400.0 True
... ... ... ...
646828 0.0 25.0 False
646829 0.0 25.0 False
646830 0.0 125.0 False
646831 0.0 875.0 False
646832 0.0 250.0 False

642934 rows × 3 columns

In [4]:
df_group11 = df_group1.groupby(["amount_reached"]).agg(ar_rate = ("amount_reached", "size"))
df_group11
Out[4]:
ar_rate
amount_reached
False 47395
True 595539
In [5]:
# plotten 

my_pie1 = px.pie(
                values=df_group11.loc[:,'ar_rate'],
                names=df_group11.index , 
                title='Are projects reached the target?',
                color=df_group11.index,  
                color_discrete_map={1:'green',0:'red'},        
                width=600,
                height=400
                )

my_pie1.update_layout(legend_traceorder="reversed",
                      legend_title_text='Are people interested in Projects?',
                      )

my_pie1.update_traces(hoverinfo='percent + label',
                      textinfo='value + percent',  
                      insidetextorientation='horizontal', 
                      textposition='inside'                
                      )  

my_pie1.data[0].labels = ['No', 'Yes']  # Labels ändern

my_pie1.show()

92% of the projects had reaached the loan amount, the target. That shows that our platform is successfull and taking the attention of people. Since this is a funding platform, even the target is not reached, summed amount is paid to borrowers. Actually we can say that it is more successfull when you think there are projects reached to %90.

Thema 2: Who are using the platform?¶

a: Gender\Country based¶

In [6]:
df_group21 = df_cf.groupby(["country"]).agg(nof_project_male = ("borrower_male_count", "sum"), \
    nof_project_female = ("borrower_female_count", "sum")).reset_index()
df_group21["total_project"] = df_group21.nof_project_female + df_group21.nof_project_male
df_group21.sort_values("total_project", ascending=False, inplace=True)
df_group21.head(5)
Out[6]:
country nof_project_male nof_project_female total_project
34 Kenya 54662.0 106152.0 160814.0
59 Philippines 8358.0 145890.0 154248.0
57 Paraguay 2523.0 116608.0 119131.0
11 Cambodia 12752.0 55523.0 68275.0
58 Peru 17356.0 45760.0 63116.0
In [7]:
wide_df = df_group21.head(20)

fig = px.bar(wide_df, x="country", y=["nof_project_male", "nof_project_female"], \
    title="Projects male versus female initiated", labels={'country':'Countries','value':'Number of Projects'})
fig.update_layout(legend=dict(title="Gender initiaded projects"))
fig.show()

It seems that our platform is used mostly by womens. Even in Kenya where women has very small power in the society women used this platform more than men. In Philippines and Paraguay men has nearly no vale versus womens. So we can focus on womens needs (reaching and using the platform) to make it more effective. And we can also focus on why men are not using as much as women. That is normal that underdeveloped countries such as Kenya Philippines, Cambodia are using the platforms to fund their needs, when they have no chance to fulfill on their own

b: Gender\Sector based¶

In [8]:
df_group22 = df_cf.groupby(["sector"]).agg(nof_project_male = ("borrower_male_count", "sum"), \
    nof_project_female = ("borrower_female_count", "sum")).reset_index()
df_group22["total_project"] = df_group22.nof_project_female + df_group22.nof_project_male
df_group22.sort_values("total_project", ascending=False, inplace=True)
df_group22.head(5)
Out[8]:
sector nof_project_male nof_project_female total_project
0 Agriculture 110612.0 258277.0 368889.0
6 Food 39538.0 284637.0 324175.0
11 Retail 26747.0 199056.0 225803.0
2 Clothing 8174.0 80106.0 88280.0
12 Services 13102.0 62573.0 75675.0
In [9]:
wide_df = df_group22.head(20)

fig = px.bar(wide_df, x="sector", y=["nof_project_male", "nof_project_female"], \
    title="Projects male versus female initiated", labels={'sector':'Sectors','value':'Number of Projects'})
fig.update_layout(legend=dict(title="Gender initiaded projects"))
fig.show()

Agriculrure and Food is the main sectors for funding platforms. Normally man is a little bit stronger in Agriculture sector versus other sectors. However even in this sectors women made great job.

Thema 3: In which Activities, Countries the platform is used basing the Sector?¶

a: Sector\Activity based¶

In [10]:
df_group3 = df_cf.loc[:,["sector", "activity", "country"]]
df_group3["cnt"] = 1
df_group31 = df_group3.groupby(["sector", "activity", "country"]).agg(nof_project = ("cnt", "sum")).reset_index() 
df_group31 = df_group31.loc[df_group31.nof_project > 0,:]
df_group31
Out[10]:
sector activity country nof_project
88 Agriculture Agriculture Albania 174
89 Agriculture Agriculture Armenia 600
90 Agriculture Agriculture Azerbaijan 70
91 Agriculture Agriculture Belize 68
92 Agriculture Agriculture Benin 2
... ... ... ... ...
212706 Wholesale Wholesale Uganda 42
212707 Wholesale Wholesale Ukraine 1
212708 Wholesale Wholesale United States 10
212710 Wholesale Wholesale Vietnam 2
212714 Wholesale Wholesale Zimbabwe 8

6240 rows × 4 columns

In [11]:
df = df_group31
fig = px.sunburst(df, path=['sector', 'activity'], color='nof_project', width=1000, height=800, 
    title="Sectors and Activities")
fig.show()

C:\Users\alfa\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

C:\Users\alfa\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

These 5 services are always gets attention for the funding platforms: Food, Agriculture, Retail, Services and Clothing. Food services are mostly for Food production/Sales requested, Agriculture for farming.

b: Sector\Country based¶

In [12]:
fig = px.treemap(df, path=[px.Constant('country'), 'sector', 'country'],
                  color='nof_project', title="Sectors and Countries")
fig.show()

C:\Users\alfa\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

C:\Users\alfa\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

C:\Users\alfa\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

Retail sector used by mostly Philippines, services mostly by Kenya, Food by Colombia.

c: Sector and Country map¶

In [13]:
df = df_group31
fig = px.scatter_geo(df, locations="country", color="sector",
                     hover_name="country", size="nof_project",
                     projection="natural earth", locationmode = 'country names')
fig.show()

When we look the platforms used countries on the world map, We see Phillipines, Kenya Cambodia, some other African, Asian, American countries.

Thema 4: In which Activities, Countries the platform is used basing the Sector?¶

In [14]:
df_group4 = df_cf.loc[:,["sector", "country", "funding_pro_lender"]]
df_group4.loc[df_group4.funding_pro_lender > 0,:]
df_group4 = df_group4.groupby(["sector", "country"]).agg(avg_fund_amount = ("funding_pro_lender", "mean")).reset_index()
df_group4 = df_group4.loc[df_group4.avg_fund_amount.isna() == False,:]
df_group4
Out[14]:
sector country avg_fund_amount
1 Agriculture Albania 31.394106
2 Agriculture Armenia 48.468452
3 Agriculture Azerbaijan 39.485279
4 Agriculture Belize 27.167610
5 Agriculture Benin 35.208199
... ... ... ...
1298 Wholesale United States 82.172058
1300 Wholesale Vietnam 35.768745
1302 Wholesale Yemen 29.761906
1303 Wholesale Zambia 27.809557
1304 Wholesale Zimbabwe 33.176941

987 rows × 3 columns

In [15]:
df = df_group4

fig = px.scatter(df, x="sector", y="avg_fund_amount", color="country",
                 size='avg_fund_amount', title="Average amount of funds for Countries and Sectors")
fig.update_layout(legend=dict(title="Countries"))
fig.add_hline(
                  y=400, 
                  line_width=3, 
                  line_dash="dash", 
                  line_color="black")
fig.show()

Average amount of Funds, very interesting is Services in Congo had 816. then clothing in Paraguay. But when we check clothing and services have higher average fund amount.

Thema 5: In which Countries and sectors we couldn't reach the target?¶

In [16]:
df_group5 = df_cf.loc[df_cf.funded_amount < df_cf.loan_amount,["sector", "activity", "country"]]
df_group5["cnt"] = 1
df_group5 = df_group5.groupby(["sector", "activity", "country"]).agg(nof_project = ("cnt", "sum")).reset_index() 
df_group5 = df_group5.loc[df_group5.nof_project > 0,:]
df_group5
Out[16]:
sector activity country nof_project
88 Agriculture Agriculture Albania 10
89 Agriculture Agriculture Armenia 134
90 Agriculture Agriculture Azerbaijan 13
94 Agriculture Agriculture Bolivia 47
96 Agriculture Agriculture Burkina Faso 4
... ... ... ... ...
204970 Wholesale Goods Distribution Zambia 3
212643 Wholesale Wholesale Colombia 4
212682 Wholesale Wholesale Pakistan 1
212683 Wholesale Wholesale Palestine 1
212708 Wholesale Wholesale United States 5

2374 rows × 4 columns

In [17]:
my_view = px.scatter(data_frame=df_group5,
                     x='nof_project',
                     y='sector',
                     color='country',
                     # color_discrete_map={'Tesla':'blue','Volkswagen':'red','BMW':'orange','Audi':'green'},
                     symbol='activity',
                     # hover_name='Model',
                     title='Number of Projects unsuccessfull') 


my_view.update_traces(marker={'size':10,
                              'line':{'width':1}})

my_view.add_vline(
                  x=350, 
                  line_width=3, 
                  line_dash="dash", 
                  line_color="black")

my_view.show()

Kenya with 1831 Farming projects is the most unsuccessful funding organisation. Thinking the weather conditions it is normal to have too many farming funding events, and as a result unreached targets. Then comes El Salvador with 1293 Housing expenses and Phillippines with General Store.